Distributed Analysis in Production with RDataFrame

The ROOT software package provides the data format used in High Energy Physics by the LHC experiments. ROOT offers a data analysis interface called RDataFrame, which has proven to adapt well to the requirements of modern physics analyses. However, with the increasing data collected by the LHC experiments, the challenge to perform an efficient analysis expands. One of the solutions to ease this challenge is the leverage of modern high-performing distributed computing environments, for which RDataFrame provides an easy-to-use interface layer - the distributed RDataFrame. In this paper, we show that the distributed RDataFrame is out of the experimental testing phase, and it is now ready for production thanks to a stabilized user interface. We delve into recent improvements of the distributed RDataFrame, including memory management, C++ code inclusion, and Pythonizations of the interface that allow running the workflows seamlessly. This includes running the distributed RDataFrame on various Analysis Facilities, which is discussed towards the end of the paper.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701007
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Distributed Analysis in Production with RDataFrame

M. M. Czurylo, D. Piparo, Vincenzo Eduardo Padulano, Andrea Maria Ola Mejicanos
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

Distributed Analysis in Production with RDataFrame

M. M. Czurylo, D. Piparo, Vincenzo Eduardo Padulano, Andrea Maria Ola Mejicanos
article en

Abstract

The ROOT software package provides the data format used in High Energy Physics by the LHC experiments. ROOT offers a data analysis interface called RDataFrame, which has proven to adapt well to the requirements of modern physics analyses. However, with the increasing data collected by the LHC experiments, the challenge to perform an efficient analysis expands. One of the solutions to ease this challenge is the leverage of modern high-performing distributed computing environments, for which RDataFrame provides an easy-to-use interface layer - the distributed RDataFrame. In this paper, we show that the distributed RDataFrame is out of the experimental testing phase, and it is now ready for production thanks to a stabilized user interface. We delve into recent improvements of the distributed RDataFrame, including memory management, C++ code inclusion, and Pythonizations of the interface that allow running the workflows seamlessly. This includes running the distributed RDataFrame on various Analysis Facilities, which is discussed towards the end of the paper.

EPJ Web of ConferencesVol. 337
European Organization for Nuclear Research (CH), Berea College (US)
Openalex Percentile: Top 68%
Distributed and Parallel Computing Systems
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Distributed Analysis in Production with RDataFrame — M. M. Czurylo, D. Piparo, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS